Research interests

  • Dialog management: Applications of machine learning; integration of expert knowledge; multi-modal dialog management; planning techniques; on-line improvement.
  • Dialog and user modeling: Tracking and quantifying uncertainty in dialog state for human/computer dialog; representational structures for dialog state; ontology integration; simulation.
  • Turn-taking: Use of continuous/incremental speech recognition for turn-taking; integration of conversation history and user model for turn-taking.
  • Confidence scoring: Machine-learning-based approaches to confidence scoring; calibration in confidence scoring; features for confidence scoring.
  • Planning under uncertainty: Markov decision processes (MDPs); partially observable Markov decision processes (POMDPs); reinforcement learning.
  • Deep learning: Methods for modeling dialog and implementing dialog control using deep learning, particularly recurrent neural networks.

Recent projects

  • Language Understanding Intelligent Service (LUIS): LUIS is a new, fast way of building language understanding models. LUIS is available at luis.ai.
  • Dialog State Tracking Challenge (DSTC): An on-going series of research community challenge task for accurately estimating a user’s goal in a spoken dialog system. DSTC homepage.


  • Microsoft Research, Principal Researcher, 2012-Present
  • AT&T Labs Research, Principal Member of Technical Staff, 2006-2012
  • Cambridge University, Ph D, Engineering Dept, 2002-2006
  • Edify Corp, Senior consultant – Usability and Speech Technology, 2002-2005
  • Tellme Networks, Voice Application Development Manager, 2000-2001
  • McKinsey & Company, Associate, 1999-2000
  • Cambridge University, Masters, Speech/Language Processing, 1998-1999
  • Princeton University, BSE, Electrical Engineering, 1994-1998


Interns (current and former)

Kavosh Asadi, Summer 2016 and 2017.
Reinforcement learning with recurrent neural networks for dialog systems.
Currently a PhD student with Michael Littman, Brown University.

Hao Fang, Summer 2015.
Natural language understanding.
Currently a PhD student with Mari Ostendorf, University of Washington.

Nobal Niraula, Summer 2014.
Natural language understanding.
Currently with Boeing Research & Technology.

Pradeep Dasigi, Summer 2014.
Conversational systems.
Currently a PhD student with Ed Hovy, CMU.

Ke Zhai, Summer 2013.
Dialog modeling.
Currently an Applied Scientist at Microsoft.

He He, Summer 2013.
Action selection in dialog systems (co-mentored with Lihong Li as lead mentor).
Currently a post-doc researcher with Percy Liang at Stanford University.

Angeliki Metallinou, Summer 2012.
Belief tracking in dialog systems (co-mentored with Dan Bohus).
Currently a Speech Scientist at Amazon.

Ethan Selfridge, Summer 2010 and 2011 at AT&T.
Turn-taking in dialog systems.
Currently at Interactions Corp.

Hamid Chinaei, Summer 2010 at AT&T.
Reinforcement learning for dialog systems.
Currently with Nuance Communications.

John Asmuth, Summer 2009 at AT&T.
Bayesian approaches to reinforcement learning.
Currently at Google.

Lihong Li, Summer 2008 at AT&T.
Feature selection in reinforcement learning.
Currently a Researcher with Microsoft Research.

Umar Syed, Summer 2007 at AT&T.
Learning user models from unlabeled data.
Currently a research scientist at Google.

Thesis committees

Ethan Selfridge, Oregon Health & Science University (2013)
Rohit Kumar, Carnegie Mellon University (2011).


8010364: System and method for applying probability distribution models to dialog systems in the troubleshooting domain

8140328: User intention based on N-best list of recognition hypotheses for utterances in a dialog

8433578: System and method for automatically generating a dialog manager

8457968: System and method for efficient tracking of multiple dialog states with incremental recombination

8473292: System and method for generating user models from transcribed dialogs

8660844: System and method of evaluating user simulations in a spoken dialog system with a diversion metric

8914288: System and method for advanced turn-taking for interactive spoken dialog systems

8954319: System and method for generating manually designed and automatically optimized spoken dialog systems

9015048: Incremental speech recognition for dialog systems

9547471: Generating computer responses to social conversational inputs

[ + 8 more patent applications in progress with the US Patent Office ]

Appointments, editor, and organizer roles

Journal reviewing and grant panelist

Conference program committees

Workshop and other program committees

  • ACL demo committee: 2011.
  • ACL Student Research workshop program committee: 2014, 2015.
  • COLING Demonstration session: 2016, 2014.
  • COLING Workshop: Spoken language technologies for pervasive speech-based and multimodal applications program committee: 2014, 2008
  • Workshop on Speech-centric Natural Language Processing, program committee: 2017 (EMNLP), 2016 (COLING)
  • Domain Adaptation for Dialog Agents Workshop (DADA): 2016
  • EACL 2014 Workshop on Dialog in Motion: 2014.
  • ECAI/IJCAI/AAAI Workshop on Machine Learning for Interactive Systems (MLIS): 2015, 2014, 2013, 2012
  • EMNLP Workshop on Modeling Large Scale Social Interaction in Massively Open Online Courses: 2014.
  • Future and Emerging Trends in Language Technologies (FELT): 2015, 2016.
  • ICML Workshop on Learning to Generate Natural Language: 2017.
  • IJCAI Workshop on Knowledge and Reasoning in Practical Dialogue Systems program committee: 2011, 2009.
  • Machine Learning for Spoken Language Understanding and Interactions Workshop (at NIPS): 2015.
  • NAACL Student Research workshop program committee: 2015.
  • NIPS Workshop on Learning Semantics: 2014.
  • RE-WOCHAT (Workshop on Collecting and Generating Resources for Chatbots and Conversational Agents – Development and Evaluation): 2016 (1), 2016 (2)
  • SLAM: Joint ISCA/IEEE International Workshop on Speech, Language and Audio in Multimedia: 2013.
  • Workshop on Future and Emerging Trends in Language Technology: 2015.
  • Workshop on Future directions and needs in the Spoken Dialog Community: Tools and Data scientific committee: 2012.
  • Workshop on Speech-Centric Natural Language Processing: 2017
  • Young researchers’ roundtable on spoken dialogue systems advisory board: 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2006.


Dialog and Conversational Systems Research

Established: March 14, 2014

Conversational systems interact with people through language to assist, enable, or entertain. Research at Microsoft spans dialogs that use language exclusively, or in conjunctions with additional modalities like gesture; where language is spoken or in text; and in a variety of settings, such as conversational systems in apps or devices, and situated interactions in the real world. Projects Spoken Language Understanding

Spoken Language Understanding

Established: May 1, 2013

Spoken language understanding (SLU) is an emerging field in between the areas of speech processing and natural language processing. The term spoken language understanding has largely been coined for targeted understanding of human speech directed at machines. This project covers our research on SLU tasks such as domain detection, intent determination, and slot filling, using data-driven methods. Projects Deeper Understanding: Moving beyond shallow targeted understanding towards building domain independent SLU models. Scaling SLU: Quickly bootstrapping SLU…



















Language Understanding Intelligent Service (LUIS)

LUIS is a new, fast way of building language understanding models.  LUIS is available at luis.ai.


Dialog State Tracking Challenge

The Dialog State Tracking Challenge (DSTC) is a research community challenge task for accurately estimating a user’s goal in a spoken dialog system. DSTC homepage.